Identification and characterization of a novel epigenetic signature in melanoma patients using liquid biopsy
Bibliographic record
Abstract
Melanoma is the most serious skin cancer worldwide and remains among the top ten newly diagnosed cancers. Despite recent advancements in immune checkpoint inhibitors (ICIs) and targeted therapies, melanoma still accounts for a significant amount of skin cancer-related deaths, highlighting the need for new, innovative therapeutic strategies. Epigenetic dysregulation through DNA methylation is a hallmark of many cancers, including melanoma, and is known to contribute to tumor formation and progression. Over the last decade, the analysis of circulating nucleic acids in plasma using liquid biopsies has gained considerable attention in oncology diagnostics due to its minimally invasive, rapid, and inexpensive sampling procedure. Considering this and recognizing that epigenetic changes are among the earliest alterations in carcinogenesis, identifying and monitoring DNA methylation biomarkers in cell-free DNA (cfDNA) through liquid biopsies offers promising potential for improving both melanoma detection and treatment. Therefore, in this thesis, we aimed to develop and validate a high-throughput targeted DNA methylation-based test for detecting melanoma in cfDNA from plasma and predicting response to ICIs. Using publicly available tumor DNA methylation databases TCGA and GEO, we used a two-pronged approach for discovering regions in DNA that are categorically differentially methylated between control and melanoma subjects. We developed two sets of markers: four pan-cancer markers for general cancer detection across 36 cancer types and one melanoma-specific marker. To examine translation of these biomarkers in a clinical setting, we developed a multiplexed next-generation sequencing assay targeting these five melanoma-specific DNA regions and tested it on a clinical cohort of 199 participants, including 121 patients across various stages of melanoma and 78 healthy donors sourced from biorepositories. Plasma cfDNA was collected from 187 subjects, and biopsies from 35 patients. After comprehensive analysis, the four identified pan-cancer DNA regions (cg10723962, cg15759056, cg24427504, cg25024074) categorically methylated in melanoma but unmethylated in other tissues showed high classification accuracy in silico (AUC = 0.9987, sensitivity 98.67%, specificity 100%). Moreover, the fifth melanoma-specific region (cg04652957) that differentiated melanoma from other cancers demonstrated similar performance (sensitivity 100%, specificity 90.91%). In the clinical plasma samples, the multiplexed assay showed very high sensitivity in biopsies of over 90%, as expected from its performance in silico, but showed lower sensitivity of 20% and 60% for early- and late-stage melanoma, respectively. In all samples, a high specificity of 98.15% was obtained. Furthermore, a significant correlation was observed between the five-gene methylation signature and response to ICIs (p = 0.012), with lower methylation scores associated with improved overall survival (log-rank p = 0.001). The distinct methylation profiles of the treatment-responsive cohort highlight the potential of the identified epigenetic signature for the prediction of response to ICI treatment. Despite low sensitivity due to the quantity of cfDNA, particularly in early stages, these findings highlight the potential of the markers for treatment response prediction and monitoring of melanoma. Overall, this assay supports the potential clinical utility of epigenetic biomarkers for melanoma, supporting their role in improving patient outcomes and reducing healthcare burdens through earlier diagnosis and personalized treatment strategies
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".